This volume contains selected short papers from the 26th International Conference on Inductive Logic Programming (ILP 2016). ILP 2016 was held in London, during September 4-6 2016, at the Warren House Conference Centre. Since its rst edition in 1991, the annual ILP conference has served as the premier international forum for learning from structured relational data. Originally focusing on the induction of logic programs, over the years it has expanded its research horizon signi cantly and welcomed contributions on all aspects of learning in logic, multi-relational data mining, statistical relational learning, graph and tree mining, learning in other (non-propositional) logic-based knowledge representation frameworks, exploring intersections with statistical learning and other probabilistic approaches. Theoretical advances in all these areas have also been accompanied by challenging applications of these techniques to important problems in elds like bioinformatics, medicine, and text mining.
With the intent of stimulating collaborations and discussion between academia and industry, the program featured three invited talks by academic and industrial distinguished researchers. In the talk Inferring Causal Models of Complex Relational and Dynamic Systems, David Jensen, from the University of Massachusetts, presented key ideas, representations and algorithms for causal inference, and highlighted new technical frontiers. Frank Wood, from the University of Oxford, gave a talk entitled Revolutionising Decision Making, Democratising Data Science, and Automating Machine Learning via Probabilistic Programming. In his talk, he gave a broad overview of the emerging eld of probabilistic programming, from the point of view of both programming (modelling) language and automated inference, and introduced most important challenges facing this eld. Finally, Vijay Saraswat, senior research scientist in the Cognitive Computing Research division at the IBM T.J. Watson Center, discussed in his talk Machine Learning and Logic the beginnings of a new computer science? the open challenges of building cognitive assistants in compliance, and the need to bring together researchers in natural language understanding, machine learning, and knowledge representation/reasoning to address them.
The conference featured, for the rst time, also an international competition, designed and managed by Mark Law, a member of our Local Committee. The competition was aimed at testing the accuracy, scalability and versatility of the learning systems which were entered. The competition had two main tracks for probabilistic and non-probabilistic approaches. The winners of the competition were Peter Schuller, from Marmara University, for his non-probabilistic approach and jointly Riccardo Zese, Elena Bellodi and Fabrizio Riguzzi for their probabilistic approach. Results of the competition are publicly available on http://ilp16.doc.ic.ac.uk/competition.
The ILP 2016 conference was kindly sponsored by IBM Watson Research, the Association of Logic Programming, Springer Lecture Notes in Arti cial Intelligence, Arti cial Intelligence journal, and Machine Learning journal. We would like to thank EasyChair for supporting submission handling. We would like to thank the members of the Local Committee of ILP 2016: Krysia Broda, Dalal Alrajeh and Mark Law. Our deep thanks also go to Mark Law for running the competition, and for setting up and maintaining the Website. The conference would not have been possible without their hard work.
Finally, we would like to thank all those involved in making ILP'16 such a success: our invited speakers, our sponsors, the program committee and of course, those who came to ILP'16 to present and discuss their work. May 2017
Organisation
Program co-Chair: Competition Chair: Financial Chair: Publicity Chair:
Alessandra Russo (Imperial College, UK) Mark Law (Imperial College, UK) Dalal Alrajeh (Imperial College, UK) Krysia Broda (Imperial College, UK)
Arti cial Intelligence Journal